International Society of Science and Applied Technologies |
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On Bayesian Estimation for Software Reliability Assessment Based on a Discrete NHPP Model | ||||
Author | Shinji Inoue
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Co-Author(s) | Shigeru Yamada
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Abstract | We discuss an interval estimation approach of parameters and software reliability assessment measures of a discretized software reliability model, which has a consistency with an actual collecting activity of discrete software fault-count data and has been expected to conduct highly-accurate assessment of software reliability. The interval estimation must be important to conduct software reliability assessment because most of software reliability data are incomplete. Concretely speaking, we apply the Markov chain Monte Carlo (MCMC) method for conducing Bayesian interval estimations for software reliability assessment. Further, this paper shows numerical examples of our approach by using actual fault count data.
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Keywords | Software reliability assessment, Software reliability model, Interval estimation, Markov chain Monte Carlo, Discretized NHPP model | |||
Article #: 23-074 |
August 3-5, 2017 - Chicago, Illinois, U.S.A. |